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Exploratories

This exploratory tells stories about cities and people living in it. Data scientists describe those territories by means of data, statistics and models. This allows citizens and local administrator to better understand cities and how to improve them.

Investigating City Mobility

How do people move into the city? How does the traffic change during the day? And how does it vary during the week? How does the tourism presence affect the traffic? Our data scientists already study the traffic in the Italian cities of Pisa and Florence by analyzing Big Data sources such as mobile phone traces, vehicular GPS and social media data as proxy of human behaviour. The results could be useful for both local administrators and citizens. The local administrators could have a tool to quantify accurately city’s traffic and understand city’s usage, so they could take better decisions to manage mobility. Citizens could take informations to know traffic situation in real time and they could choose the best and fastest way. Our studies could be useful in carpooling, too. Indeed, Big Data analysis can suggest to citizens who can share the travel with them.

Can Big Data help us to understand relationships between economy and daily life habits? Has there been a significant variation in price in the last few years? How does poverty affect people sociability? We investigates the changes in people’s and companies behavior after the economic crisis and their correlation. We also study the measurement of the real cost of life by studying the price variation. We try to correlate people well-being with their social and mobility data, discovering that they change in poor areas. This approach can potentially lead to development of effective policies in order to reduce internal and external risks of the companies, leading to a systematic improvement of well-being.

Integrated Business and Economics

What is the best composition of a company board? Does the gender distribution of employees influence the company success? Does the presence of a good company influence people that live in a certain territory? In this story we want to understand the relationship between the distribution of gender and age in boards of companies and the improved credit risk management. We try to find out if the differences lead an advantage. Our aim is also to create wellness indexes in order to study the effects of the presence of a successful company on people that live near it.

By analysing discussions on social media and newspaper articles, in this exploratory we study public debates to understand which are the most discussed topics. We can identify themes, following the discussions around them and tracking them through time and space.

Polarised Political Debates

How does people discuss on online social networks? Who are online social network users that take part in political debate? What is the structure of their social network? In this story we focus on online debates about discussing topics. By using data from online social networks, we analyze people discussions about topics that are relevant for society. We will investigate who attends debates, how people discuss, and what are their social relationships compared with those of people of different views. We try to understand also how politicians discuss and they influence other people. We made visualization to represent the user social network and topics evolution.

Monitoring Topics across Time and Space

What does newspapers talk about? Is it possible to understand when a specific theme becomes relevant for the media? Are there some topics that become interesting for all journalists at the same time? In this story we analyze german newspaper articles to understand the most relevant topics and monitoring how they are discussed by press. We made a visualization that shows the most frequent topics in a certain period of time. In our visualization each word is weighed on the basis of its meaning and it has reference on space and time. Thanks to this study it twill be possible to identify interesting topics automatically, before to start studies each topic.

We try to estimate flows and stocks from available data in real time, by building models that map observed measures extracted from unconventional data sources to official data. Since migration might generate cultural changes on the local and incoming population, we evaluate the migrants integration in new communities through social network and retail data analysis. Furthermore, SoBigData.it supports datajournalism projects on migration. We partner with the team of “Demal Te Niew”, the webdocumentary on migration between Italy and Senegal published on L’Espresso and El Pais.

Unveiling the patterns of success in sports

The proliferation of new sensing technologies that provide high-fidelity data streams extracted from every game is changing rapidly the way scientists, fans and practitioners conceive sports performance. By combining these (big) data with the powerful tools of data science and AI, we have now the possibility to unveil the great complexity underlying sports performance and perform many challenging tasks: from automatic tactical analysis to data-driven performance ranking, game outcome prediction and injury forecasting. Our data scientists in Pisa are using massive data describing several sports – especially soccer, cycling and rugby – to construct interpretable and easy-to-use tool for sports coaches and managers. Our studies open an interesting perspective on how to understand the factors influencing sports success and how to build simulation tools for boosting both individual and collective performance.

We are evolving, faster than expected, from a time when humans are coding algorithms and carry the responsibility of the resulting software quality and correctness, to a time when sophisticated algorithms automatically learn to solve a task by observing many examples of the expected input/output behavior. Most of the times the internal reasoning of these algorithms is obscure even to their developers. For this reason, the last decade has witnessed the rise of a black box society. Black box AI systems for automated decision making, often based on machine learning over big data, map a user's features into a class predicting the behavioral traits of individuals, such as credit risk, health status, etc., without exposing the reasons why. This is troublesome not only for lack of transparency but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. It is therefore urgent to develop a set of techniques which allows the user to understand why an algorithm made a decision.

Exploratories and Thematic areas

SoBigData covers six thematic areas: text and social mining, social network, human mobility, web analytics, visual analytics and social data. These general areas are presented in specific vertical applications in specific fields to help better explore the potentiality of them in real world problems.
In the SoBigData Resource Catalogue you can navigate the methods and datasets in both the dimensions or filter by any combinations of the two.

About SoBigData.eu

SoBigData.eu receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 654024
The views and opinions expressed in this website are the sole responsibility of the author and do not necessarily reflect the views of the European Commission.